Computational Intelligence in Control

The problem of controlling uncertain dynamic systems, which are subject to external disturbances, uncertainty and sheer complexity is of considerable interest in computer science, Operations Research and Business domains. The application of intelligent systems has been found useful in problems when the process is either difficult to model or difficult to solve by conventional methods. Intelligent systems have attracted increasing attention in recent years for solving many complex problems. Computational Intelligence in Control will be a repository for the theory and applications of intelligent systems techniques in modelling control and automation. Computational.Intelligence.In.Control 1 Cover 1 Table of Contents 5 Preface 8 SECTION I: NEURAL NETWORKS DESIGN, CONTROL AND ROBOTICS APPLICATION 11 Chapter I: Designing Neural Network Ensembles by Minimising Mutual Information 12 Chapter II: A Perturbation Size- Independent Analysis of Robustness in Neural Networks by Randomized Algorithms 33 Chapter III: Helicopter Motion Control Using a General Regression Neural Network 52 Chapter IV: A Biologically Inspired Neural Network Approach to Real-Time Map Building and Path Planning 80 SECTION II: HYBRID EVOLUTIONARY SYSTEMS FOR MODELLING, CONTROL AND ROBOTICS APPLICATIONS 98 Chapter V: Evolutionary Learning of Fuzzy Control in Robot-Soccer 99 Chapter VI: Evolutionary Learning of a Box-Pushing Controller 115 Chapter VII: Computational Intelligence for Modelling and Control of Multi-Robot Systems 133 Chapter VIII: Integrating Genetic Algorithms and Finite Element Analyses for Structural Inverse Problems 146 SECTION III: FUZZY LOGIC AND BAYESIAN SYSTEMS 157 Chapter IX: On the Modelling of a Human Pilot Using Fuzzy Logic Control 158 Chapter X: Bayesian Agencies in Control 178 SECTION IV: MACHINE LEARNING, EVOLUTIONARY OPTIMISATION AND INFORMATION RETRIEVAL 192 Chapter XI: Simulation Model for the Control of Olive Fly Bactrocera Oleae Using Artificial Life Technique 193 Chapter XII: Applications of Data-Driven Modelling and Machine Learning in Control of Water Resources 207 Chapter XIII: Solving Two Multi-Objective Optimization Problems Using Evolutionary Algorithm 228 Chapter XIV: Flexible Job-Shop Scheduling Problems: Formulation, Lower Bounds, Encoding and Controlled Evolutionary Approach 243 Chapter XV: The Effect of Multi-Parent Recombination on Evolution Strategies for Noisy Objective Functions 272 Chapter XVI: On Measuring the Attributes of Evolutionary Algorithms: A Comparison of Algorithms Used for Information Retrieval 289 Chapter XVII: Design Wind Speeds Using Fast Fourier Transform: A Case Study 311 About the Authors 331 Index 343 "The problem of controlling uncertain dynamic systems, which are subject to external disturbances, uncertainty and sheer complexity is of considerable interest in computer science, Operations Research and Business domains. The application of intelligent systems has been found useful in problems when the process is either difficult to model or difficult to solve by conventional methods. Intelligent systems have attracted increasing attention in recent years for solving many complex problems. Computational Intelligence in Control will be a repository for the theory and applications of intelligent systems techniques in modelling control and automation."--From publisher This chapter describes negative correlation learning for designing neural network ensembles.